Comparison of Artificial Neural Network and SDSM Methods in the Downscaling of Annual Rainfall in the HadCM3 Modelling (Case study: Kerman, Ravar and Rabor)

Nowadays, it is believed that anthropogenic activities, such as changes in land use and deforestation, have resulted in atmospheric concentrations of greenhouse gases. One consequence of this ruinous activity, is an alteration of the energy balance that tends to warm the atmosphere that has resulted...

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Main Authors:	M. Rezaee, M. Nahtaj, A. Moghadamniya, A. Abkar
Format:	Article
Language:	fas
Published:	Marvdasht Branch, Islamic Azad University 2015-04-01
Series:	مهندسی منابع آب
Subjects:	precipitation climate change downscaling hadcm3 model
Online Access:	https://wej.marvdasht.iau.ir/article_874_b14fc5b5c7f5ad223367662203996774.pdf

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author	M. Rezaee M. Nahtaj A. Moghadamniya A. Abkar M. Rezaee
author_facet	M. Rezaee M. Nahtaj A. Moghadamniya A. Abkar M. Rezaee
author_sort	M. Rezaee
collection	DOAJ
description	Nowadays, it is believed that anthropogenic activities, such as changes in land use and deforestation, have resulted in atmospheric concentrations of greenhouse gases. One consequence of this ruinous activity, is an alteration of the energy balance that tends to warm the atmosphere that has resulted in climate change. Precipitation forecast is one of the most important element in water resources management and planning. In this study, precipitation depth of Kerman, Ravar and Rabor Stations have been predicted using the HadCM3 model outputs under the A2 scenario, SDSM downscaling models and artificial neural network, for three periods: 2010-2039, 2040-2069 and 2070-2099. Precipitation data for the 1971- 2001 period were selected as the base one. The results obtained by using the two models were evaluated and compared according to the statistical criteria. The artificial neural network model showed superior performance for the Kerman and Ravar stations. Annual precipitation of Kerman, Ravar and Rabor stations by 2099, using the AMM model decreases by 12.86, 11.68, and 11.39 percentage points, respectively. These are for 0.89, 18.48, and 1.55 percentage points, respectively, for the same year.
first_indexed	2024-03-08T15:28:56Z
format	Article
id	doaj.art-d6630864488a46c9b85c640b65fbfa84
institution	Directory Open Access Journal
issn	2008-6377 2423-7191
language	fas
last_indexed	2024-03-08T15:28:56Z
publishDate	2015-04-01
publisher	Marvdasht Branch, Islamic Azad University
record_format	Article
series	مهندسی منابع آب
spelling	doaj.art-d6630864488a46c9b85c640b65fbfa842024-01-10T08:07:10ZfasMarvdasht Branch, Islamic Azad Universityمهندسی منابع آب2008-63772423-71912015-04-018242540874Comparison of Artificial Neural Network and SDSM Methods in the Downscaling of Annual Rainfall in the HadCM3 Modelling (Case study: Kerman, Ravar and Rabor)M. Rezaee0M. Nahtaj1A. Moghadamniya2A. Abkar3M. Rezaee4دانش آموخته کارشناسی ارشد، گروه مرتع و آبخیزداری ، دانشگاه زابلاستادیار، گروه مرتع و آبخیزداری دانشگاه زابلدانشیار، گروه احیای مناطق خشک و کوهستانی ، پردیس کشاورزی و منابع طبیعی دانشگاه تهرانکارشناس ارشد مرکز تحقیقات کشاورزی و منابع‌طبیعی استان کرمانمربی گروه مهندسی برق و کامپیوتر، دانشگاه سیستان و بلوچستانNowadays, it is believed that anthropogenic activities, such as changes in land use and deforestation, have resulted in atmospheric concentrations of greenhouse gases. One consequence of this ruinous activity, is an alteration of the energy balance that tends to warm the atmosphere that has resulted in climate change. Precipitation forecast is one of the most important element in water resources management and planning. In this study, precipitation depth of Kerman, Ravar and Rabor Stations have been predicted using the HadCM3 model outputs under the A2 scenario, SDSM downscaling models and artificial neural network, for three periods: 2010-2039, 2040-2069 and 2070-2099. Precipitation data for the 1971- 2001 period were selected as the base one. The results obtained by using the two models were evaluated and compared according to the statistical criteria. The artificial neural network model showed superior performance for the Kerman and Ravar stations. Annual precipitation of Kerman, Ravar and Rabor stations by 2099, using the AMM model decreases by 12.86, 11.68, and 11.39 percentage points, respectively. These are for 0.89, 18.48, and 1.55 percentage points, respectively, for the same year.https://wej.marvdasht.iau.ir/article_874_b14fc5b5c7f5ad223367662203996774.pdfprecipitationclimate changedownscaling hadcm3 model
spellingShingle	M. Rezaee M. Nahtaj A. Moghadamniya A. Abkar M. Rezaee Comparison of Artificial Neural Network and SDSM Methods in the Downscaling of Annual Rainfall in the HadCM3 Modelling (Case study: Kerman, Ravar and Rabor) مهندسی منابع آب precipitation climate change downscaling hadcm3 model
title	Comparison of Artificial Neural Network and SDSM Methods in the Downscaling of Annual Rainfall in the HadCM3 Modelling (Case study: Kerman, Ravar and Rabor)
title_full	Comparison of Artificial Neural Network and SDSM Methods in the Downscaling of Annual Rainfall in the HadCM3 Modelling (Case study: Kerman, Ravar and Rabor)
title_fullStr	Comparison of Artificial Neural Network and SDSM Methods in the Downscaling of Annual Rainfall in the HadCM3 Modelling (Case study: Kerman, Ravar and Rabor)
title_full_unstemmed	Comparison of Artificial Neural Network and SDSM Methods in the Downscaling of Annual Rainfall in the HadCM3 Modelling (Case study: Kerman, Ravar and Rabor)
title_short	Comparison of Artificial Neural Network and SDSM Methods in the Downscaling of Annual Rainfall in the HadCM3 Modelling (Case study: Kerman, Ravar and Rabor)
title_sort	comparison of artificial neural network and sdsm methods in the downscaling of annual rainfall in the hadcm3 modelling case study kerman ravar and rabor
topic	precipitation climate change downscaling hadcm3 model
url	https://wej.marvdasht.iau.ir/article_874_b14fc5b5c7f5ad223367662203996774.pdf
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Comparison of Artificial Neural Network and SDSM Methods in the Downscaling of Annual Rainfall in the HadCM3 Modelling (Case study: Kerman, Ravar and Rabor)

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